Method to characterize material using mathematical propagation models and ultrasonic signal

ABSTRACT

The invention is directed to a system and method for detecting defects in a manufactured object. These defects may include flaws, delaminations, voids, fractures, fissures, or cracks, among others. The system utilizes an ultrasound measurement system, a signal analyzer and an expected result. The signal analyzer compares the signal from the measurement system to the expected result. The analysis may detect a defect or measure an attribute of the manufactured object. Further, the analysis may be displayed or represented. In addition, the expected result may be generated from a model such as a wave propagation model. One embodiment of the invention is a laser ultrasound detection system in which a laser is used to generate an ultrasonic signal. The signal analyzer compares the measured ultrasonic signal to an expected result. This expected result is generated from a wave propagation model. The analysis is then displayed on a monitor.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of and claims the benefit of priorityto U.S. patent application Ser. No. 09/996,098, U.S. Pat. No. 6,856,918,entitled “Method to Characterize Material Using Mathematical PropagationModels and Ultrasonic Signal”, filed on Nov. 26, 2001, and isincorporated herein by reference in its entirety.

BACKGROUND

1.Field of the Invention

The present invention generally relates to a method to characterize amaterial using ultrasound measuring devices. In particular, the presentinvention relates to detecting defects in a material by comparing theresults of a mathematical model and an ultrasonic signal emitted duringlaser ultrasound testing.

2.Description of Prior Art

Ultrasound testing methods are non-invasive, generally non-destructive,techniques used to measure features of materials. These features mayinclude layer thickness, cracks, delamination, voids, disbonds, foreigninclusions, fiber fractions, fiber orientation, and porosity. Thefeatures may influence a given material's qualities and performance ingiven applications. Each application places unique demands on thematerial's qualities including the need for differing strength,flexibility, thermal properties, cost, or ultraviolet radiationresistance. With the changing demands, more non-invasive,non-destructive testing of materials is being performed using techniquessuch as ultrasound testing.

Ultrasound testing includes transducer-induced, laser andplasma-initiated ultrasound. Transducer-induced ultrasound techniquesuse piezoelectric transducers to induce an ultrasonic signal in anobject.

Laser ultrasound techniques use a laser pulse. When the laser pulse isdirected at an object, it causes thermal expansion in a small region.This thermal expansion causes ultrasonic waves. These ultrasonic wavesare then measured by a detector and converted into information about thefeatures of the object. The laser pulse may be generated by severallasers including a ruby laser, a carbon laser, and a Nd:YAG laser.

In some cases, a higher laser-energy density can be used and some matterat the material surface is ablated. The recoil effect of the pulverizedmatter launches ultrasonic waves in the material. Similarly to thethermoelastic regime, this ablation regime produces ultrasonic wavesthat can be detected and converted into information about the featuresof the object.

Similar to the laser ultrasound, plasma-induced ultrasound causesthermal expansion initiated ultrasonic waves. Often, a laser generatesthe plasma by directing a pulse at a false target in proximity to themanufactured object. The plasma then hits the manufactured object,producing an ultrasonic wave.

The manufactured object may be composed of different materials includingmetal, polymer, composite, or ceramic materials. The detector may be oneof several devices. For example, the detector may be a transducer on thesurface of the object, a laser interferometer directed at the object, ora gas-coupled laser acoustic detector, to name a few.

Ultrasound techniques are applied in research as well as industrialsettings. In research, ultrasound techniques are used to test newmaterials for desired features. The technique is used to seek defects inmaterial that has undergone stress or environmental endurance testing.In an industrial setting, the technique is used during scheduledservicing or during manufacturing to inspect parts for defects.Aircraft, automobile and other commercial industries have shownincreasing interest in these techniques.

However, one difficulty associated with ultrasound techniques is foundin discerning information about the features of the object from themeasured ultrasonic waves. Many of the objects are constructed fromcomposite materials with multiple layers. As the waves traverse thematerial, they reflect off interfaces or defects, propagate at differingspeeds within different layers and change amplitude. The measured signalis a complex compilation of these reflections, ultrasonic velocitydifferences and amplitude changes. More layers and differing materialsadd to the complexity. In general, an expert is required to discernrelevant aspects of the complex ultrasound signal.

One approach used by experts is to determine which peaks within thesignal signify a reflection off of the back surface of the object. Theexpert then looks for smaller peaks between the back surface reflectionpeaks to determine number of layers or other structural features. Thedistance between smaller peaks or the amplitude of these peaks yieldsinformation about the thickness of a layer, the composition of thelayer, or the interface between layers.

By implication, ultrasound techniques require a great deal of expertise.This requirement limits the broad application of ultrasonic techniquesin industrial settings and makes the technique expensive. Anotherproblem is the amount of time associated with translating an ultrasoundsignal into understandable information about the features within theobject. Long translation times lead to expensive labor costs and reducednumbers of tests.

As such, many ultrasound techniques suffer from difficulties associatedwith translating complex ultrasound signals. Many other problems anddisadvantages of the prior art will become apparent to one skilled inthe art after comparing such prior art with the present invention asdescribed herein.

SUMMARY OF THE INVENTION

Aspects of the invention are found in a system and method for detectinga physical attribute of a manufactured object. The system includes anultrasound testing device, a signal analyzer, and an expected result.The signal analyzer is coupled to the ultrasound testing device. Inoperation, the ultrasound measuring device detects a signal indicativeof the manufactured object. The ultrasound measuring device generates ameasured signal. Further, the signal analyzer compares the measuredsignal to the expected result.

Other aspects may be included on an as needed basis. For example, amodel processor may be included to generate the expected result. A modelprocessor may, for example, be a computer programmed with a mathematicalmodel of ultrasound propagation. The expected result may, for example,be calculated using an ultrasonic propagation mathematical model and theexpected characteristics of the manufactured object.

Further, a representation of the manufactured object may be included.The model processor may generate the expected result from therepresentation of the manufactured object. In a further example, therepresentation of the manufactured object may be a computer-aided-design(CAD) representation of the manufactured object.

Further, the model processor may, using the mathematical model, extractthe relevant information from the ultrasonic signal with or withouta-priori knowledge of the manufactured object. As an example, the modelprocessor may extract from the ultrasonic signal the number of layersand the position of a defect in the manufactured object. One exemplarymethod for obtaining the relevant information is to generate an expectedultrasonic signal using a propagation model that approximates a measuredexperimental signal. By manipulating parameters of the propagationmodel, the output from the propagation model nearly approximates themeasured signal. The parameters used in matching the measuredexperimental signal are then indicative of the manufactured object. Assuch, the model parameters may be used to plot representations of themanufactured object. Additionally, they may be compared to an acceptablerange of parameters. If the value of the parameters is outside thatrange, a defect is detected.

A programmable circuitry may also be connected to the model processor.The model processor may generate the expected result with theprogrammable circuitry.

Another aspect of the invention is the signal analyzer. The signalanalyzer compares a measured result to an expected result. In thismanner, the signal analyzer detects the physical attribute of themanufactured object. Further, the signal analyzer may detect thephysical attribute of the manufactured object with respect to a modelrepresentation.

As such, a system and method for detecting a physical attribute in amanufactured object is described. Other aspects, advantages and novelfeatures of the present invention will become apparent from the detaileddescription of the invention when considered in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a system for detecting physicalattributes of a manufactured object according to the invention.

FIG. 2 is a schematic block diagram of an exemplary embodiment of thesystem of FIG. 1.

FIG. 3 is a schematic block diagram of another exemplary embodiment ofthe system of FIG. 1.

FIG. 4 is a schematic block diagram of another exemplary embodiment ofthe system of FIG. 1.

FIG. 5 is a schematic block diagram of a further exemplary embodiment ofthe system of FIG. 1.

FIG. 6 is a block schematic diagram detailing how components of FIG. 1may be implemented.

FIG. 7 is a block flow diagram of an exemplary method which may be usedby the systems of FIGS. 1, 2, and 3.

FIG. 8 is a block flow diagram of an exemplary method which may be usedby the systems of FIGS. 4, and 5.

FIG. 9 is a time series graph of a exemplary comparison between anexpected result and a signal as recited in the method of FIG. 4.

FIG. 10 is a time series graph of a exemplary comparison between anexpected result and an experimental signal from a multi-layer samplewithout defect as may result from the methods of FIGS. 7 and 8.

FIG. 11 is a time series graph of a exemplary comparison between anexpected result and an experimental signal from a multi-layer samplewith a defect as may result from the methods of FIGS. 7 and 8.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a schematic block diagram of a system for detecting physicalattributes of a manufactured object 14 according to the invention. Asdepicted, the system 10 has a measurement system 12, a signal analyzer16, and an expected result 18. The signal analyzer 16 is coupled to themeasurement system 12. The expected result 18 is in communication withthe signal analyzer 16. In practice, the signal analyzer, measurementsystem and the expected result may be encompassed in the same apparatus,separately housed, or constructed in various combinations.

The measurement system 12 detects a sonic energy signal 20 in themanufactured object 16. Then, the signal analyzer 16 receives the signaland automatically compares the signal to the expected result 18. Bycomparing the signal and the expected result 18, the signal analyzer 16may detect the physical attribute of the manufactured object 14.Further, this comparison may be iterative wherein the signal is comparedto a plurality of expected results 18 and/or an adjusted expected result18.

The measurement system 12 may use various means for detecting the sonicenergy signal 20. These means may include one or more piezoelectrictransducers, one or more electromagnetic transducers, a laserinterferometer, or a gas-coupled laser acoustic detector, to name a few.

The signal analyzer 16 may manipulate the measured signal and expectedresult 18 in various ways. These manipulations may include a simplesubtraction of the expected result 18 from the signal, transformation ofthe time domain signal into a frequency domain, or filtering the signal,among others.

The analysis of the signal may also be performed in several ways. Forexample, an expert system may pick relevant amplitude peaks in thesignal. The analysis may also include comparisons between a frequencydomain transformation of the signal and the expected result 18.Additionally, the analysis may identify peaks, measure time differencesbetween peaks, watch for missing peaks, or other analysis activities.Each of these activities may be automated. Further, the analysis mayinvolve comparing the signal in one of many forms to a plurality ofexpected results 18.

These analysis activities may result in the detection of a physicalattribute of the manufactured object. This physical attribute may be aflaw, delamination, void, fracture, fissure, or crack, among others.Early defect detection helps greatly in materials research.Additionally, in an industry like aircraft servicing, early detection ofdefects could improve safety and prevent a catastrophic failure.

In a similar manner to that described above, the system 10 may also beused for testing a physical feature. These physical features may includenumber of layers, layer thickness, fiber number, fiber orientation, andporosity, to name but a few. Testing materials for physical featureshelps in materials research and for industry. The development ofmaterials customized for specific applications will lead to improvedsafety, better product quality, and lower cost products.

For testing, the system 10 measures the sonic energy signal 20indicative of the physical feature. The sonic energy signal 20 may beinduced in several ways. It may be induced by a transducer-inducedultrasound technique, a plasma-induced ultrasound technique or a laserultrasound technique, among others.

Similar to detection, in testing, the sonic energy signal 20 is measuredby the measurement system 12. The signal analyzer 16 compares the signalfrom the measurement system 12 to the expected result 18.

The sonic energy signal 20 may be measured in one of several ways. Forexample the sonic energy signal 20 may be measured by one or morepiezoelectric transducers, one or more electromagnetic transducers, alaser interferometer, or a gas-coupled laser acoustic detector, to namebut a few.

The expected result 18 may be the outcome of a previous test of the sameobject. Alternatively, this result may be the output from a predictivepropagation model, an automated expert system, results of a previoustest of a similar object, or a set of parameters sought by an expert,among others. The parameters may represent an amplitude setting, alocation in a time series graph, a frequency in a frequency domaintransformation, or others. Further, the expect result 18 may be testresults of a know test model or a known imperfect object. In addition,the expected result 18 may the output of a model. Further, the model maybe iteratively adapted to approximate the signal. As a result,parameters of the model may be indicative of the physical attribute ofthe manufactured object.

This system may be used to test, measure and detect physical featuresand attributes. For example, the system may be used to detect a flaw orvoid, among others. The system may also be used to measure a layerthickness, fiber orientation, or porosity.

FIG. 2 is a schematic block diagram of an exemplary embodiment of thesystem of FIG. 1. A system 30 for detecting physical attributes of amanufactured object 34 has a measurement system 32, a signal analyzer36, and an expect result 38, as found in the system of FIG. 1. Inaddition, the system 30 of FIG. 2 may have a model processor 40, aprogrammable circuitry 42, a representation of the manufactured object44, and a display 46.

Similar to the system of FIG. 1, the measurement system 32 communicateswith the signal analyzer 36. The expected result 38 is coupled to thesignal analyzer 36. As shown, the model processor 40 may be coupled tothe expected result 38. However, the model processor 40 may communicatedirectly with the signal analyzer 36.

The representation of the manufactured object 44 is accessible to themodel processor 40. This may be accomplished as acomputer-aided-drafting representation in a memory storage, data on anetwork device, a file on drive or a simultaneously generatedrepresentation, among others. However, it too may be configured in otherways. For example, the representation of the manufactured object 44 maycommunicate directly with the programmable circuitry 42.

The programmable circuitry 42 is shown as part of the model processor40. However, this circuitry may be separate from the model processor 40.The programmable circuitry 42 may provide adaptable functionality to thesystem. For example, the programmable circuitry 42 may permit models,parameters, and configurations to be interchanged in the system. Theprogrammable circuitry 42 may allow programs and instruction sets to beswapped as desired.

The display 46 may be included to provide a representation of theresults of the signal analysis. This display 46 may communicate with thesignal analyzer 36. However, it may also communicate with the modelprocessor and/or the expected result, to name a few.

The embodiment of FIG. 2 operates in a similar manner to that of FIG. 1.The measurement system 32 detects an sonic energy signal 48 such as anultrasound signal. The signal analyzer 36 automatically compares thesonic energy signal 48 to the expected result 38 and produces ananalysis result. The analysis result may be represented on the display46.

For example, an interferometer may measure a sonic energy signal, suchas an ultrasound signal. The interferometer may detect distortions in areflected laser beam. The distortions may be converted to a time domainvoltage signal. This voltage signal may be received by a signalanalyzer. The signal analyzer may compare the voltage signal to anexpected result 38. For example, the expected result 38 may be theoutput of a wave propagation model.

Additionally, the expect result 38 used in the analysis may be anoutcome of a previous test. The result 38 may also be the output of anexpert system, the solution to a propagation model, or a set ofparameters sought by an expert, to name but a few. The parameters,above, may represent an amplitude setting or a location in a time seriesgraph.

The expected result may be generated by the model processor 40. Themodel processor 40 may, for example, use a propagation model or expertsystem, among others, to generate the expected result 38. The modelprocessor may also determine the expected result 38 from arepresentation of the manufactured object 44. In addition, the modelprocessor 40 may generate the expected result 38 with a programmablecircuitry 42.

For example, the model processor 40 in communication with theprogrammable circuitry 42 may be a computer with software appropriatefor generating the results of a propagation model. However, the modelprocessor 40 may also be a computer with an expert system or an analogcircuitry, to name a couple alternate examples.

The programmable circuitry 42 may be reprogrammed with a hand helddevice, over a network, by direct action through a keyboard, or throughother means. The outcome is an adaptable circuitry and an adaptablesystem 30.

The representation of the manufactured object 44 may be found in manyforms. These forms may include a computer-aided-drafting representation,a data map associated with the manufactured object, or a set ofparameters characteristic of the material, among others. The parametersabove may be number of layers, types of material, velocities of soundthrough different materials, object size, and key dimensions, amongothers.

The display 46 may also take many forms. These forms may include amonitor on a computer, an oscilloscope, a printer, a television screen,a visual or sonic alarm, or others. The display may be a C-scan, aB-scan or others. Additionally, the display may represent the results ofthe signal analysis in relation to images of the object. Further, thedisplay may represent the results of the signal analysis in a 3-Drepresentation relative to the geometry of the manufactured object.

Various combinations or connections are imagined. The components may beimplemented separately, or in various combinations.

In the manner described above, the system 30 of FIG. 2 can detect aphysical feature in a manufactured object or test a manufactured objectfor physical attributes. As stated above, the detection of a featurelike a defect could improve safety and prevent catastrophic failure.This system also accelerates testing and reduces reliance on experts.Ultimately, the method reduces the cost, making testing more practical.Lower cost testing will lead to broader application in materialsresearch and service safety inspections. As a result, this system couldhave a significant impact on safety and performance of high costproducts like airplanes and automobiles.

FIG. 3 is a schematic block diagram of another exemplary embodiment ofthe system of FIG. 1. In this embodiment, the system 70 has a laserultrasound measuring device 72, a signal analyzer 76, an expected result78, a model processor 80, and a representation of the manufacturedobject 82. The system 70 may also have a display 86 and a programmablecircuitry 84.

As in the systems of FIG. 1 and FIG. 2, a sonic energy signal 89 fromthe manufactured object 74 is measured by the laser ultrasound measuringdevice 72. In the embodiment of FIG. 3, the sonic energy signal isinitiated by a laser pulse 88. The laser ultrasound measuring device 72measures the initiated sonic energy signal and produces a measuredsignal. Then, the signal analyzer 76 automatically compares the measuredsignal from the laser ultrasound measuring device 72 and the expectedresult 78. The outcome of the comparison may then be displayed on thedisplay 86.

The expected result 78 may be generated by the model processor 80. Thismodel processor 80 may use a programmable circuitry 84 and/or arepresentation of the object 82 to generate the expect result 78.

For example, the model processor 80 may be computer. The programmablecircuitry 84 may hold software and the representation of themanufactured object 82 may be a computer-aided-design (CAD)representation of the manufactured object. The computer may use asoftware-encoded ultrasound wave propagation model and the CADrepresentation of the object to generate an expected propagation wave.However, the model processor 80, programmable circuitry 84, andrepresentation of the manufactured object 82 may take many differentforms.

FIG. 4 is a schematic block diagram of another exemplary embodiment ofthe system of FIG. 1. The system 110 has a measuring device 112, asignal analyzer 116, and a model processor 120. The measuring device 112is connected to the signal analyzer 116. The signal analyzer 116communicates with the model processor 120.

In addition, one or more estimated parameters 118 are accessible by themodel processor 120. Further, these estimated parameters 118 may bedisplayed on a display 120. The estimated parameters 118 may be accessedby the display 120.

Further, the model processor 120 may have a programmable circuitry 124.The model processor 120 and the programmable circuitry 124 may take manyforms, including those forms described above and below, among others.

These elements may be configured as shown. Other elements such as arepresentation of the manufactured object may also be included. Inaddition, these and other elements may function in many otherconfigurations. For example, the estimated parameters 118 and thedisplay 126 may be accessible by the signal analyzer 116.

The measuring device 112 may measure a signal 129. A signal generatormay be included as needed to generate the signal through interaction128. However, the signal may be generated through alternate means.

The signal analyzer 116 may then compare the signal to the output fromthe model processor 120. The model processor 120 may generate an outputfor comparison. The signal analyzer 116 may direct the model processor120 to produce the output. Further, the model processor 120 mayiteratively produce the output, adapt the output, or change the outputin response to a signal from the signal analyzer 116.

In addition, the model processor 120 may use the programmable circuitry124 to produce, adapt, and/or change the output. Further, the modelprocessor 120 may access the estimated parameters 118 in performing itsfunction. Furthermore, the model processor 120 may access arepresentation of the manufactured object in determining the output.

For example, the signal analyzer 116 may compare a measured signal to anoutput from the model processor 120. The signal analyzer 116 may thensend a direction and/or output to the model processor 120. The directionmay, for example, take the form of a command to iterate or re-determinethe output. The output may, for example, take the form of a signalindicating the level of similarity between the measured signal and theoutput from the model processor 120.

As such, the model processor 120 may, for example, access the estimatedparameters 118. Further, the model processor 120 may change theestimated parameters 118. For example, the model processor 120 may adaptparameters in a model used to determine the output. The model may, forexample, be a sonic propagation model. The model processor 120 mayiteratively change parameters of the model to generate an outputapproximating the measured signal. As such, the model processor 120 mayuse and/or change the estimated parameters 118. These estimatedparameters 118 may converge to values indicative of the manufacturedobject. Further, the resulting estimated parameters 118 may be accessedby a display.

Alternatively, the model processor 120 and/or the signal analyzer 116may compare the measured signal to a set of predetermined outcomes.These outcomes may have associated estimated parameters 118. Theseoutcomes may, for example, be indicative of known physical attributes.For example, a known defect may be characterized by a known signal. Themeasured signal may be compared to a set of outcomes indicative offlawless, and various types of flaws, among others.

In addition, the parameters 118 may be accessed by the display 126.Further, the display 126 may be accessed and/or may access the signalanalyzer 116 and/or the model processor 120.

FIG. 5 is a schematic block diagram of a further exemplary embodiment ofthe system of FIG. 1. The system 130 has many of the same elements ofthe system described in FIG. 4. These elements may be in theconfiguration shown in FIG. 4 and/or in FIG. 5. Alternatively, theseelements may configured in various ways.

In the exemplary embodiment of FIG. 5, a range of parameters 142 isdepicted. This range of parameters 142 may be accessed by the modelprocessor 120. However, it may also be accessed by the signal analyzer136 and/or the display 146.

In this exemplary embodiment, the system 130 may function to iterativelydetermine estimated parameters 138 as described above. Further, a set ofoutputs may be compared to the measured signal to determine theestimated parameters 138.

These estimated parameters 138 may be compared to the range ofparameters 142 or an expected parameter. This comparison may beperformed by the model processor 120 and/or the signal analyzer 136.Further the comparison may be accessed by the display 146.

This comparison may be indicative of the manufactured object. Forexample, if the estimated parameters 138 are within the range ofparameters 142 or match the expected parameter, the manufactured objectmay, for example, meet tolerance ranges set for quality control.However, these ranges may alternatively indicate a flaw, type of flaw, aphysical attribute, or others. For example, the range of parameters 142may be indicative of a void, a number of layers, a fraction of fibers,and others.

FIG. 6 is a block schematic diagram detailing how components of FIG. 1may be implemented. A signal analyzer 92 and a model processor 94 may beimplemented on the same device 90. A programmable circuitry 96 may alsobe implemented on the device 90. In addition, a storage medium 98 may becontained in the device 90. The storage medium 98 may hold an expectedresult 100, a representation of a manufactured object 102, a new signalfunction 104, and a new model function 106. Further, the storage medium98 may hold an estimated parameter.

The signal analyzer 92, model processor 94, and programmable circuitry96 may function as described in FIG. 1, FIG. 2, and FIG. 3. Similarly,the expected result 100 and the representation of the manufacturedobject 102 may take the forms described above. The new signal 104 mayreplace or change the signal analyzer's 92 method of operation. Further,the new model function 106 may replace or change the model processor's94 method of operation. The new model function 106 may also replace themodel and model parameters used by the model processor 94.

An exemplary embodiment consistent with the description above is acomputer with a microprocessor and a memory. Software on the computerwould perform the functions of the signal analyzer 92 and the modelprocessor 94. The software would access the expected result 100 and therepresentation of the manufactured object 102 to perform the functions.Further, the software may change the signal analyzer's 92 function andthe model processor's 94 function by accessing the new signal function104 and the new model function 106, respectively.

This example is intended to illustrate one possible embodiment of theinvention, among others. Therefore, the invention is not limited to thisexample. Each of the items listed may be housed together, separately orin any combination. Each item may also be included on an as neededbasis.

FIG. 7 is a block flow diagram of an exemplary method which may be usedby the systems of FIG. 1, FIG. 2 and FIG. 3. In a block 52, a sonicenergy signal is measured from the manufactured object. In a subsequentblock 54, the signal is analyzed by comparing the signal to an expectedresult. As seen in block 56, this expected result may be generated orpreviously generated and accessed here. In the next block 58, the resultof the comparison or the physical attribute detected through thecomparison may be represented on a display.

As described in the systems of FIG. 1, FIG. 2 and FIG. 3, the signalmeasurement may utilize various techniques. These techniques may includeone or more piezoelectric transducers, one or more electromagnetictransducers, a laser interferometer, or a gas-coupled laser acousticdetector, to name but a few.

The step of analyzing the signal may be accomplished by manipulating thesignal and expected result in several ways. These manipulations mayinclude a simple subtraction of the expected result from the signal,transformation of the time domain signal into a frequency domain, orfiltering the signal, among others. The analysis of the signal may alsobe performed by an expert system picking relevant amplitude peaks in thesignal. Further, the analysis may include comparisons between afrequency domain transformation of the signal and the expected result.Additionally, the analysis may identify peaks, measure time differencesbetween peaks, or watch for missing peaks, to name but a few analysisactivities. The result of the analysis may be the detection of aphysical attribute or a comparison of the signal and the expectedresult.

As above, the expected result may be a stored result or it may begenerated. The generation of the expected result may be performed priorto the analysis of the signal or in parallel with the signal analysis.The generation step may be accomplished through several means such asusing a model processor. For example, the model processor may use apropagation model or expert system, among others, to generate theexpected result. The model processor may also determine the expectedresult from a representation of the manufactured object.

In a further block 58, the result of analyzing the signal may berepresented and/or displayed. The physical attribute or the analysis maybe displayed using several methods. For example, these methods mayinclude a monitor on a computer, an oscilloscope, a printer, a visual orsonic alarm, a television screen, or others.

Using a method similar to the method of FIG. 7, the benefits of thesystems above may be realized. As a result, products with greater safetyand higher quality will be made available at lower costs.

FIG. 8 is a block flow diagram of an exemplary method which may be usedby the systems of FIGS. 4, and 5. In the method 150, a signal ismeasured as depicted in a block 152. In a block 154, the expected signalis generated. The expected signal may be generated sequential to themeasurement, in coincidence with the measurement, and before themeasurement, among others. Further, the expected signal may be a singleexpected output, the first in an iterative set of model outputs, one ofa predetermined set of output, or others.

The measured signal and the expected signal may the be compared as shownin a block 156. In the case of an iterative model or comparison with apredetermined set, the comparison may yield an indication as to whetherthe signals are similar. This indication may, for example, by a measureof difference such as a mean square difference, a comparison of peakamplitudes, a comparison of features, and others. However, thecomparison may take various forms and should not be limited by theexamples.

If the signals are not similar, a new parameter may be determined asshown in a block 160. Alternatively, a new predetermined output may beselected. The expected signal may be generated again and selectivelycompared in an iterative manner. The system may keep track of the numberof iterations. After a predetermined number of iterations, the modelmight be considered as unable to generate an expected signal consideredas similar to the experimental signal, a defect is flagged.

If the signals are similar, the parameters may be compared to a range ofparameters. If the adjusted parameters or, alternately, those of theselected predetermined output are within the range of parameters, themanufactured object may be within quality tolerances, for example.However, the range may be indicative of a flaw, a type of flaw, lack ofa flaw, a physical attribute, and others.

Further, the process may be selectively repeated for various regionsabout a manufactured object. The term “about” may mean in, on, or inproximity to. For example, the manufactured object may be tested bymeasuring a sonic signal from various regions of the manufacturedobject. The method above may be repeated for each and/or selectedregions.

FIG. 9 is a time series graph of an exemplary comparison between anexpected result and a signal as recited in the method of FIGS. 7 and/or8. The solid line represents a measured signal. The dashed linerepresents an expected result. The star may also represent an expectedresult where the expected result is simply an expected peak location andamplitude.

The signal may be initiated by the means described above. These meansmay include a transducer, a laser pulse, or a plasma pulse, amongothers. The signal may also be measured by methods recited above. Thesemethods may be one or more transducers, a laser interferometer, or agas-coupled laser acoustic detector, to name a few.

As previously described, the expected result may be one of a previousmeasurement, a result of a mathematical model, an expert system, orothers. In FIG. 9, the result is shown as either a previous measurementor a result of an ultrasound generation and propagation model. However,the expected result is not limited by these examples and may take otherforms.

In FIG. 9, the dashed and solid lines show several interactions that mayindicate a physical attribute. For example, at a point A, the expectedresult and signal meet and begin a rise to a peak. Differences in thebeginning of the rise in the signal and that in the expected result maybe indicative of several features. For example, the offsets in beginningtime may indicate differing layer thickness between that used by apropagation model and the actual material. The absence of an expectedpeak may indicate a void between layers.

At a point B, both the expected result and the signal reach a maximumamplitude or peak. At this peak, the expected result has a higheramplitude than the signal. This disparity between peak amplitudes may beindicative of incorrect predictions about acoustic properties.

At another point C, the expect result and the signal both oscillate.However, they may oscillate with differing periods, amplitudes, andphase angles. For example, differing oscillations may indicate theexistence of a small layer, such as an epoxy layer, between two largerlayers. The small layer may not have been included in the propagationmodel from which the expected result was derived.

The expected result may also predict an inverse wave or no wave wherethe signal shows one to exist. For example, at point D, the expectedresult predicts a small amplitude positive wave and the signal shows alarge amplitude negative wave. The situation may also be reversed wherethe signal shows the small amplitude positive wave and the expectedresult predicts a large amplitude negative wave. In this example, anunanticipated layer, such as an epoxy layer, may cause a difference inthe expected results and the signal.

In addition, the expected result may take another form. For example, theexpected result may be an expected peak amplitude and location asindicated by another point E. A comparison may be a least squaresdifference between the signal and the expected point E, a determinationas to whether the peak crossed the amplitude of the expected point E, atest to determine whether the peak occurred before or after the expectedpoint E, or others.

If comparisons similar to the examples above were performedautomatically during testing, defects would be detected consistently andwithout the labor of an expert. Tests would be performed faster and withless expense. Lower cost would make flaw detection more economical foruse in safety inspections and material testing. As a result, the systemand method recited above would lead to greater safety and may preventcatastrophic accidents.

The points described above are presented for illustrative purposes. Manypossible comparisons may be performed between measured signals andresults from propagation models. Also, the explanations of the phenomenapresented above are not necessarily indicated by those phenomena nor arethe phenomena exclusive to the physical features mentioned in theexamples. All, some, or none of the phenomena may be seen in any givencomparison between measured sonic signals and expected results.

FIG. 10 is a time series graph of a exemplary comparison between anexpected result and an experimental signal from a multi-layer samplewithout defect as may result from the methods of FIGS. 7 and 8. Acomparison may, for example, be made between the two signals.

For example, the lack of a flaw may be determined by a comparison offeatures. Further, the location of the features in time and/or theamplitude of the features may be indicative of the object. For example,the expected signal may be a predetermined signal generated by a model.Alternatively, the expected signal may be previous measurement of themanufactured object or a known sample of a similar manufactured object.The comparison may indicate the lack of a flaw

In addition, the expected signal may be the result of a model. The modelparameters may be iteratively and/or adaptively adjusted in order thatthe expected result approximate the measured signal. The modelparameters used to determine the expected result may be indicative themanufactured object. If these parameters are within a given range, themanufactured object may, for example, pass a quality test.

Further, the comparison of the signals may yield information as to thephysical attribute and/or features of the manufactured object. Forexample, the number of layers, the thickness of layers, the orientationof fiber, and others may be determined through the comparison.

Furthermore, the comparison may be quantified through many techniques.These techniques may include a difference, a comparison of featureamplitudes, a mean square difference, a least square difference, anmaximum difference, and others.

However, the expected signal may be determined from many sources.Further, FIG. 10 is an exemplary time series graph. The comparison maybe performed using other graph types and other methods.

FIG. 11 is a time series graph of a exemplary comparison between anexpected result and an experimental signal from a multi-layer samplewith a defect as may result from the methods of FIGS. 7 and 8. Acomparison may, for example, be made between the two signals.

For example, the flaw may be determined by a comparison of features.Further, the location of the features in time and/or the amplitude ofthe features may be indicative of the flaw. For example, the expectedsignal may be a predetermined signal generated by a model.Alternatively, the expected signal may be a previous measurement of themanufactured object or a known sample of a similar manufactured objectwith a flaw. The comparison may indicate the type of flaw

In addition, the expected signal may be the result of a model. The modelparameters may be iteratively and/or adaptively adjusted in order thatthe expected result approximate the measured signal. The modelparameters used to determine the expected result may be indicative themanufactured object with a flaw. If these parameters are within oroutside a given range, the manufactured object may, for example, fail aquality test. Further, the range of parameters may indicate the type ofdefect.

Furthermore, the comparison may be quantified through many techniques.These techniques may include a difference, a comparison of featureamplitudes, a mean square difference, a least square difference, amaximum difference, and others.

However, the expected signal may be determined from many sources.Further, FIG. 10 is an exemplary time series graph. The comparison maybe performed using other graph types and other methods.

As such, a system and method for detecting attributes of a manufacturedobject are described. In view of the above detailed description of thepresent invention and associated drawings, other modifications andvariations will now become apparent to those skilled in the art. Itshould also be apparent that such other modifications and variations maybe effected without departing from the spirit and scope of the presentinvention as set forth in the claims which follow.

1. A method for detecting an internal physical attribute of amanufactured object using an energy measuring device, the methodcomprising: measuring energy reflected from within the manufacturedobject with the energy measuring device to obtain a measured complexsignal indicative of a structural defect within the manufactured object,the internal physical composition of the manufactured object comprisingnon-homogeneous material; comparing the measured signal to an expectedresult associated with the manufactured object; determining thestructural defect based on the step of comparing; and displaying thedetermined structural defect.
 2. The method of claim 1, wherein thecomplex measured signal is a complex compilation of reflections,velocity differences, and amplitude changes, the method furthercomprising: generating the expected result from a mathematical model ofultrasonic propagation and expected characteristics of the manufacturedobject.
 3. The method of claim 1, wherein the non-homogeneous materialcomprises material having multiple layers, the method furthercomprising: deriving the expected result from empirical tests.
 4. Themethod of claim 1, the method further comprising: displaying thestructural defect in relation to at least one image of the manufacturedobject.
 5. The method of claim 4, wherein the display is a C-scan. 6.The method of claim 4, wherein the display is a B-scan.
 7. The method ofclaim 4, wherein the structural defect is displayed as a threedimensional image positioned relative to geometry of the manufacturedobject.
 8. A system for the detection of an internal physical attributeof a manufactured object, the system comprising: a sonic measuringdevice operable to perform the following operations: detecting a complexsignal indicative of a structural defect in the manufactured object, themanufactured object comprising a non-homogeneous material, andgenerating a measured result associated with the signal; an expectedresult; and a signal analyzer, communicatively coupled to the sonicmeasuring device, that is operable to perform the following operations:receiving the measured result; comparing the expected result to themeasured result responsive to receiving the measured result, andproducing a comparison of the measured result and the expected result.9. The system of claim 8, further comprising: a model processorcommunicatively coupled to the signal analyzer; and the model processoradapted to generate the expected result from a representation of themanufactured object.
 10. The system of claim 9, further comprising: aprogrammable circuitry coupled to the model processor; and the modelprocessor adapted to generate the expected result with the programmablecircuitry.
 11. The system of claim 9, wherein the non-homogeneousmaterial object comprises material having multiple layers; wherein thesonic measuring device comprises at least one of the following: a laserinterferometer or a laser acoustic detector; wherein the representationof the manufactured object is stored on a readable medium, the readablemedium being communicatively coupled to the model processor; and whereinthe model processor is operable to perform the operation of generatingthe expected result from the stored representation of the manufacturedobject.
 12. A system for comparing measurements from an ultrasoundtesting system, the ultrasound testing system testing a manufacturedobject for internal physical characteristics and detecting a signalgenerated on or in the manufactured object, the system comprising: asignal analyzer that compares a predetermined expected result with ameasured result to determine an internal structural defect in the nonmanufactured object; the manufactured object comprising anon-homogeneous material having multiple layers; the measured resultassociated with the signal detected by the ultrasound testing system;and the predetermined expected result associated with the manufacturedobject.
 13. A method for detecting an internal physical attribute of amanufactured object using a sonic measuring device, the sonic measuringdevice operable to perform the operation of measuring sonic energy fromthe manufactured object and obtaining a measured signal, the methodcomprising: comparing the measured signal to an expected result todetect an internal physical attribute of the manufactured object, themanufactured object comprising non-homogeneous material, the expectedresult derived from a model or representation of the manufacturedobject; determining the internal physical attribute based on the step ofcomparing; and displaying information associated with the internalphysical attribute.
 14. The method of claim 13, wherein the manufacturednon-homogeneous material comprises having multiple layers; and whereinthe comparison is selectively repeated, the expected result beingrepeatedly generated from an iteratively adapted mathematical model, thecomparison being selectively repeated until a quantifier indicative ofthe, comparison has a predetermined value.
 15. The method of claim 13,the method further comprising: deriving the expected result fromempirical tests.
 16. The method of claim 13 wherein the comparison isselectively repeated, the expected result being selected from a set ofpredetermined expected results, the comparison being selectivelyrepeated until a quantifier indicative of the comparison has apredetermined value.
 17. A system for the detection of an internalphysical attribute of a manufactured object, the system comprising: asignal analyzer communicatively coupled to a sonic measuring device andoperable to receive a measured signal, wherein the sonic measuringdevice comprises a laser interferometer or laser acoustic detector thatmeasures sonic energy from the manufactured object and produces themeasured signal; an expected result of the manufactured object; thesignal analyzer operable to perform the following operations: comparingthe expected result to the measured result automatically to detect aninternal physical attribute, and producing a comparison of the measuredsignal and the expected result.
 18. The system of claim 17, furthercomprising: a model processor communicatively coupled to the signalanalyzer, and the model processor adapted to generate the expectedresult from a representation of the manufactured object.
 19. The systemof claim 18, wherein the representation of the manufactured object is acomputer-aided-drafting representation of the manufactured object. 20.The system of claim 18, further comprising: a programmable circuitrycommunicatively coupled to the model processor; and the model processoradapted to generate the expected result with the programmable circuitry.21. The system of claim 18, wherein the representation of themanufactured object is stored on a readable medium, the readable mediumbeing communicatively coupled to the model processor and the modelprocessor generating the expected result from the stored representationof the manufactured object.
 22. The system of claim 17, furthercomprising: a display communicatively coupled to the signal analyzer;and the display adapted to display the comparison of the measured signaland the expected result.
 23. A system for the detection of an internalphysical attribute of a manufactured object comprising non-homogeneousmaterial, wherein a sonic measuring device measures a sonic energy fromthe manufactured object and produces a measured signal, the systemcomprising: a signal analyzer communicatively coupled to the sonicmeasuring device and operable to receive the measured signal; a modelprocessor communicatively coupled to the signal analyzer to provide anoutput indicative of an expected result of the manufactured object; andthe signal analyzer operable to perform the operations of: comparing anoutput of the model processor to the measured result automatically todetect an undesirable internal physical attribute, and producing acomparison of the measured signal and the output of the model processor.24. A method for detecting an internal physical attribute of amanufactured object comprising non-homogeneous material, wherein a sonicmeasuring device measures a sonic energy from the manufactured objectand produces a measured signal, the method comprising: comparing themeasured signal to an output from a model processor with a signalanalyzer to detect an internal physical attribute, the output indicativeof an expected result of the manufactured object; producing an outputfrom the signal analyzer indicative of the step of comparing; anddisplaying the output from the signal analyzer.
 25. The method of claim24, wherein the steps of comparing and producing are selectivelyrepeated, the comparing being of the measured signal and the output fromthe model processor, the model processor selectively changing the outputin response to the output from the signal analyzer.
 26. The method ofclaim 25, wherein the model processor selectively changes the output toone of a plurality of predetermined outputs.
 27. A program storagedevice readable by a machine, tangibly embodying a program ofinstructions executable by the machine to perform the method steps fordetecting an internal physical attribute of a manufactured object usinga sonic measuring device, the internal physical composition of themanufactured object comprising non-homogeneous material, the sonicmeasuring device measuring sonic energy from the manufactured object andobtaining a measured complex signal, said method steps comprising:comparing the measured complex signal indicative of a structural defectwithin the manufactured object comprising non-homogeneous material to anexpected result; determining the structural defect based on the step ofcomparing; and displaying information associated with the structuraldefect.